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Mestrado Bolonha em Economia e Gestão de Ciência, Tecnologia e Inovação A ciência cidadã consiste numa forma de investigação que inclui cidadãos/ãs não investigadores/as no processo científico, procurando facilitar de forma inovadora o diálogo da ciência com a sociedade. A presente dissertação interroga-se acerca deste paradigma que reflete a transformação da relação entre ciência-sociedade, e que coloca desafios tanto no processo como nos resultados obtidos (a capacidade dos vários atores participantes, o nível de colaboração e de intervenção no processo e a qualidade e a natureza dos resultados alcançados). Para compreender como funciona a ciência cidadã, a tese usa o método de estudo de caso. O caso analisado corresponde a um projeto internacional em curso- o projeto eGroundwater- que aplica a ciência cidadã em contexto escolar no âmbito do problema da degradação dos aquíferos, em particular do aquífero de Campina-Faro (Algarve). Apoiandose, em parte, em Capdevilla et al., (2020) e Kieslinger et al. (2018), o trabalho levanta as seguintes questões: (1) quais as entidades e os atores participantes que integram este projeto de ciência cidadã no contexto escolar, e que atributos os/as caraterizam? (2) quais os processos postos em prática na implementação de projetos de Ciência Cidadã nas escolas? e (3) que impactos se podem esperar de projetos de ciência cidadã com escolas? O trabalho é amplamente exploratório e qualitativo pois usa a observação etnográfica e entrevistas realizadas a professores participantes no projeto. Cruzando os vários registos sobre este estudo de caso, observou-se que a operacionalização de um projeto de Ciência Cidadã depende da conjugação de processos, como o treino e a comunicação entre partes, como também da natureza dos atributos dos atores e entidades integrantes e do seu desenvolvimento. Finalmente, depreende-se que é possível criar conhecimento científico a partir da cooperação entre universidades e escolas, empoderando professores na sua atividade letiva, estimulando a reflexão dos estudantes para a cidadania, e expandindo o acesso dos investigadores a novos dados. Citizen science is a form of research that includes citizens in the scientific process, seeking to facilitate the dialogue between science and society in an innovative way. This dissertation questions this paradigm that reflect the transformation of the relationship between science and society, and that poses challenges both in the process and in the results obtained (the capacity of the various participating actors, the level of collaboration and intervention in the process and the quality and nature of the results achieved). To understand how citizen science works, the thesis uses the case study method. The analysed case corresponds to an ongoing international project - the eGroundwater project - which applies citizen science in a school context within the scope of the problem of aquifer degradation, in particular the Campina-Faro aquifer (Algarve). Relying, in part, on Capdevilla et al., (2020) and Kieslinger et al. (2018), the work raises the following questions: (1) which entities and participating actors are part of this citizen science project in a school context, and what attributes characterize them? (2) what are the processes put into practice in the implementation of Citizen Science projects in schools? and (3) what impacts can be expected from citizen science projects with schools? The work is largely exploratory and qualitative as it uses ethnographic observation and interviews with teachers participating in the project. Crossing the various records on this case study, it was observed that the implementation of a Citizen Science project depends on the combination of processes, such as training and communication between parties, as well as on the nature of the attributes of the actors and constituent entities and their development. Finally, it appears that it is possible to create scientific knowledge from cooperation between universities and schools, empowering teachers in their teaching activity, stimulating students' reflection on citizenship, and expanding researchers' access to new data. info:eu-repo/semantics/publishedVersion

Three-dimensional subsurface elastic models inverted from seismic reflection data are the basis of the geo-modeling workflow. These models are often used to predict the spatial distribution of reservoir rock properties such as porosity, volume of minerals and fluid saturations. Stochastic seismic inversion methods are important modeling tools, as they allow one to infer high-resolution subsurface models and assess uncertainties related to the spatial distribution of the inverted petro-elastic properties. Within this framework, iterative geostatistical seismic inversion methods use stochastic sequential simulation and co-simulation as a model generation and perturbation technique based on the mismatch between synthetic and real seismic data. This work proposes an alternative approach of iterative geostatistical seismic inversion based on the concept of self-updating of local probability distributions of the elastic property of interest to be inverted. The model perturbation is conditioned by local probability distribution functions, which are iteratively updated based on the data misfit at previous iterations. This approach allows for better exploration of the model parameter space, avoiding local fast convergence at early steps of the inversion, and wider exploration of the model parameter space. The method is applied to a two-dimensional nonstationary synthetic dataset and to a three-dimensional real case example with a blind well test.

Geostatistical seismic inversion methods use stochastic sequential simulation as the model generation and perturbation technique. These stochastic simulation methods use a global variogram model to express the expected spatial continuity pattern of the subsurface elastic properties of interest. The conditioning to a single variogram model is not suitable for complex and non-stationary geological environments, resulting in poor inverted models unable to reproduce non-stationary features such as channels, folds, and faults. The proposed method uses a stochastic sequential simulation and co-simulation method able to cope with spatially varying information using local and independent variogram models. The information about the dip, azimuth, and ranges of the local variogram model is inferred directly from the observed data. First, local dip and azimuth structural volumes are computed from seismic attribute analysis. Then, local variogram models are fitted along the directions estimated from the previous step. This information is used as steering data during the inversion, acting as proxy of the true subsurface geological complexities. Application examples in synthetic and real datasets with complex geometries show the impact of using local anisotropy models in both the reproduction of the original seismic data and the reliability of the inverted models. The resulting inverted models show enhanced consistency, where small-to-large scale discontinuities and complex geometries are better reproduced, allowing reducing the spatial uncertainty associated with the subsurface properties. This work represents a step forward in integrating geological consistency into geostatistical seismic inversion, surpassing the limitation of using a single variogram model to reproduce complex geological patterns.

Geostatistical seismic inversion uses stochastic sequential simulation and co-simulation as techniques to generate and perturb subsurface elastic models. These steps are computational demanding and, depending on the size of the inversion grid, time consuming. This paper introduces a technique to achieve considerable reductions in the consumption of computational resources of geostatistical seismic inversion without compromising the quality of the inverse elastic models. We achieve these improvements by reducing one of the dimensions of the data domain to a periodic function approximated by a Fourier series of n terms. Then, instead of simulating over the entire original data volume, in the original data domain, we simulate independently each term of the series over the area of interest. If the number of terms in the series is considerably smaller than the size of the collapsed data domain, then the computational overhead of the inversion procedure decreases. Symbolic regression is used to obtain automatically the approximation to the periodic function that captures the behavior of the property in the reduced dimension. The advantages related to the use of symbolic regression over alternative machine learning algorithms are discussed. We use the method to simulate acoustic impedance in a geostatistical seismic inversion of a synthetic dataset representing a hydrocarbon reservoir, where the true acoustic impedance model is available. Results demonstrate a considerable speedup over traditional methods while achieving similar performance in terms of the misfit between real and synthetic seismic and a good representation of the true impedance model. The frequency content of the resulting inverted data is discussed and compared with the one inferred from traditional methods, demonstrating an expected reduction of the high-frequency component.

Iterative geostatistical seismic inversion integrates seismic and well data to infer the spatial distribution of subsurface elastic properties. These methods provide limited assessment to the spatial uncertainty of the inverted elastic properties, overlooking alternative sources of uncertainty such as those associated with poor well-log data, upscaling and noise within the seismic data. We express uncertain well-log samples, due to bad logging reads and upscaling, in terms of local probability distribution functions (PDFs). Local PDFs are used as conditioning data to a stochastic sequential simulation algorithm, included as the model perturbation within the inversion. The problem of noisy seismic and narrow exploration of the model parameter space, particularly at early steps of the inversion, is tackled by the introduction of a cap on local correlation coefficients responsible for the generation of the new set of models during the next iteration. A single geostatistical framework is proposed and illustrated with its application to a real case study. When compared against a conventional iterative geostatistical seismic inversion, the integration of additional sources of uncertainty increases the match between real and inverted seismic traces and the variability within the ensemble of models inverted at the last iteration. The selection of the local PDFs plays a central role in the reliability of the inverted elastic models. Avoiding high local correlation coefficients at early stages of the inversion increases convergence in terms of global correlation between synthetic and real seismic reflection data at the end of the inversion., 13 Figures

Conventional multi-channel seismic reflection data, known as seismic oceanography, has recently been used for the qualitative interpretation of meso- to large-scale hydrographic structures of interest. Seismic oceanography has been successfully imaging oceanographic structures in an intermediate scale not sampled by traditional oceanographic tools, such as conductivity, depth and temperature measurements and eXpendable BathyThermograph (XBT) data. However, few attempts have been made for successfully quantifying ocean properties, such as ocean temperature and salinity, directly from the seismic reflection data. This work presents an iterative geostatistical methodology capable of inverting conventional seismic oceanographic data simultaneously for high-resolution temperature and salinity ocean models. The proposed methodology was developed and implemented in a real set of contemporaneous XBT data and two-dimensional seismic profile acquired southwest of Portugal. The resulting high-resolution temperature and salinity models reproduce existing XBT data not used to constrain the geostatistical inversion, which permits reliable quantification of the ocean properties of interest.

Seismic inversion plays an important role in reservoir modelling and characterization due to its potential for assessing the spatial distribution of the subsurface petro-elastic properties. Seismic AVA inversion methodologies allow to retrieve P-wave and S-wave velocities and density individually allowing a better characterization of existing litho-fluid facies. We present an iterative geostatistical seismic AVA inversion algorithm that inverts pre-stack seismic data, sorted by angle gather, directly for: density; P-wave; and S-wave velocity models. The proposed iterative geostatistical inverse procedure is based on the use of stochastic sequential simulation and co-simulation algorithms as the perturbation technique of the model parameter space; and the use of a genetic algorithm as a global optimizer to make the simulated elastic models converge from iteration to iteration. All the elastic models simulated during the iterative procedure honour the marginal prior distributions of P-wave velocity, S-wave velocity and density estimated from the available well-log data, and the corresponding joint distributions between density versus P-wave velocity and P-wave versus S-wave velocity. We successfully tested and implemented the proposed inversion procedure on a pre-stack synthetic dataset, built from a real reservoir, and on a real pre-stack seismic dataset acquired over a deep-water gas reservoir. In both cases the results show a good convergence between real and synthetic seismic and reliable high resolution elastic subsurface Earth models. This article is protected by copyright. All rights reserved

Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.

Geostatistical seismic inversion methods are routinely used in reservoir characterization studies because of their potential to infer the spatial distribution of the petro-elastic properties of interest (e.g., density, elastic and acoustic impedances) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and global spatial continuity model as estimated from the available well-log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous and in many cases non-stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, application of a single distribution function and spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a 3D geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi-distribution functions and spatial continuity models under non-stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non-stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of a higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques. This article is protected by copyright. All rights reserved

Most geostatistical estimation and simulation methodologies assume the experimental data as hard measurements, meaning that the measures of a given property of interest are not associated with uncertainty. The challenge of integrating uncertain experimental data at the geostatistical estimation or simulation models is not new. Several attempts have been made, either considering the uncertain data as soft data or interpreting it as inequality constraints, based on the indicator formalism or decreasing the weight of soft data in kriging procedures. This paper presents a stochastic simulation methodology where the uncertain experimental data are modelled by a probability distribution at each sample location. Data values are firstly drawn, by stochastic simulation, at these locations prior to the simulation of the rest of the grid nodes. This method is also extended to the simulation of categorical uncertain data, as well as to the simulation with uncertain block support data. To illustrate the proposed methodology, an application to a real case study of pore pressure prediction of oil reservoirs is presented, as well as an upscaling problem.

Summary In geostatistical seismic inversion methods the model perturbation and update is performed by stochastic sequential simulation and co-simulation algorithms in a regular Cartesian grid and using a global variogram model to describe the spatial continuity pattern of the subsurface petro-elastic property. These approaches do not capture heterogeneous small-scale features being hard to be reproduced when dealing to highly non-stationary geological environments. This work integrates local anisotropy steering volumes to describe local anisotropies within iterative geostatistical seismic inversion methods. The incorporation of local structural and spatial information allow to obtain more consistent spatial distribution of rock properties while avoids any transformation of inversion grid during simulation process by traditional geostatistical simulation techniques. The proposed methodology of this work was successfully applied to synthetic and real application examples.

Stochastic sequential simulation is a common modelling technique used in Earth sciences and an integral part of iterative geostatistical seismic inversion methodologies. Traditional stochastic sequential simulation techniques based on bi-point statistics assume, for the entire study area, stationarity of the spatial continuity pattern and a single probability distribution function, as revealed by a single variogram model and inferred from the available experimental data, respectively. In this paper, the traditional direct sequential simulation algorithm is extended to handle non-stationary natural phenomena. The proposed stochastic sequential simulation algorithm can take into consideration multiple regionalized spatial continuity patterns and probability distribution functions, depending on the spatial location of the grid node to be simulated. This work shows the application and discusses the benefits of the proposed stochastic sequential simulation as part of an iterative geostatistical seismic inversion methodology in two distinct geological environments in which non-stationarity behaviour can be assessed by the simultaneous interpretation of the available well-log and seismic reflection data. The results show that the elastic models generated by the proposed stochastic sequential simulation are able to reproduce simultaneously the regional and global variogram models and target distribution functions relative to the average volume of each sub-region. When used as part of a geostatistical seismic inversion procedure, the retrieved inverse models are more geologically realistic, since they incorporate the knowledge of the subsurface geology as provided, for example, by seismic and well-log data interpretation.

We have developed a new iterative geostatistical seismic amplitude variation with angle (AVA) inversion algorithm that inverts prestack seismic data, sorted by angle gathers, directly for high-resolution density, P-wave velocity, S-wave velocity, and facies models. This novel iterative geostatistical inverse procedure is based on two key main principles: the use of stochastic sequential simulation and cosimulation as the perturbation technique of the model parameter space and a global optimizer based on a crossover genetic algorithm to converge the simulated earth models toward an objective function, in this case, the mismatch between the recorded and synthetic prestack seismic data. As a geostatistical approach, all the elastic models simulated during the iterative procedure honors the well-log data at its own locations, the marginal prior distributions of P-wave velocity and S-wave velocity, and density estimated from the available well-log data, and the corresponding joint distributions between density versus P-wave velocity and P-wave versus S-wave velocity. We successfully tested and implemented this new algorithm on a synthetic prestack data set that mimicked the main properties of a real reservoir, and on a real seismic data set acquired over a deepwater turbidite oil reservoir. In both cases, the results showed a good convergence between the recorded and synthetic seismic. The synthetic example showed high-resolution inverted petroelastic models that reproduced the true petroelastic models. The inverted petroelastic models retrieved from the real case study found high resolution and do agree with previous seismic reservoir characterization studies.

Iterative geostatistical history matching techniques based on stochastic sequential simulation, update the model parameters at each iteration globally or using a regional perturbation criterion (e.g., Voronoi polygons centred ad the well locations). While these techniques ensure the convergence of the iterative procedure towards the observed production data, the exploration of the model parameters, and the uncertainty space, is limited by each region considered. To avoid this effect, we propose the use of stochastic sequential simulation conditioned to local probability distributions at each grid cell to account for local uncertainties. The local probability distribution functions are built at the end of each iteration from the petrophysical realisations that generated simulated production curves with a misfit score below a given threshold. The proposed methodology was applied in a challenging non-stationary synthetic reservoir and the results show the advantages of the proposed technique when reproducing the geological features of interest when compared against conventional iterative geostatistical history matching.

Seismic inversion is an established approach to model the geophysical characteristics of oil and gas reservoirs, being one of the basis of the decision making process in the oil&gas exploration industry. However, the required accuracy levels can only be attained by dealing and processing significant amounts of data, often leading to consequently long execution times. To overcome this issue and to allow the development of larger and higher resolution elastic models of the subsurface, a novel parallelization approach is herein proposed targeting the exploitation of GPU-based heterogeneous systems based on a unified OpenCL programming framework, to accelerate a state of art Stochastic Seismic Amplitude versus Offset Inversion algorithm. To increase the parallelization opportunities while ensuring model fidelity, the proposed approach is based on a careful and selective relaxation of some spatial dependencies. Furthermore, to take into consideration the heterogeneity of modern computing systems, usually composed of several and different accelerating devices, multi-device parallelization strategies are also proposed. When executed in a dual-GPU system, the proposed approach allows reducing the execution time in up to 30 times, without compromising the quality of the obtained models. HighlightsNovel approach to accelerate a Stochastic Seismic AVO Inversion algorithm.Exploitation of GPU-based heterogeneous systems based on a unified OpenCL framework.Multi-device parallelization strategies to tackle system heterogeneity.The adopted parallelization strategy ensures the quality of the inversion results.Performance speedup as high as 30i? is obtained with a dual-GPU system.

The new ESA Sentinel-2 satellite delivers images of the red-edge band at a spatial resolution of 20m. These bands are particular useful for vegetation monitoring in general and present high potential of application in precision agriculture. For this, we propose a data fusion methodology for downscaling rededge bands to 10 m spatial resolution using the information of visible and near infrared band. The methodology is based on inverse modelling by combining geostatistical stochastic simulation methods and genetic programming. A case study case presents the preliminary results.

Improving the performance and robustness of algorithms on new high-performance parallel computing architectures is a key issue in efficiently performing 2D and 3D studies with large amount of data. In geostatistics, sequential simulation algorithms are good candidates for parallelization. When compared with other computational applications in geosciences (such as fluid flow simulators), sequential simulation software is not extremely computationally intensive, but parallelization can make it more efficient and creates alternatives for its integration in inverse modelling approaches. This paper describes the implementation and benchmarking of a parallel version of the three classic sequential simulation algorithms: direct sequential simulation (DSS), sequential indicator simulation (SIS) and sequential Gaussian simulation (SGS). For this purpose, the source used was GSLIB, but the entire code was extensively modified to take into account the parallelization approach and was also rewritten in the C programming language. The paper also explains in detail the parallelization strategy and the main modifications. Regarding the integration of secondary information, the DSS algorithm is able to perform simple kriging with local means, kriging with an external drift and collocated cokriging with both local and global correlations. SIS includes a local correction of probabilities. Finally, a brief comparison is presented of simulation results using one, two and four processors. All performance tests were carried out on 2D soil data samples. The source code is completely open source and easy to read. It should be noted that the code is only fully compatible with Microsoft Visual C and should be adapted for other systems/compilers.

We present a new iterative geostatistical seismic inversion algorithm that allows retrieving: density; P-wave velocity; S-wave velocity and facies models. This novel procedure is based on two key main ideas: stochastic sequential simulation and co-simulation as the perturbation technique of the model parameter space; and a genetic algorithm that act as a global optimizer to converge the iterative procedure towards an objective function, the mismatch between recorded and synthetic pre-stack seismic data. At the end of each iteration, the triplet of elastic traces that jointly produce synthetic gathers with the highest correlation coefficient are the basis for generating the new elastic models of the next iteration. The iterative procedure finishes when the global correlation between recorded and inverted seismic data is above a certain threshold. All the elastic models simulated during the iterative procedure honor the marginal and joint distributions of P-wave velocity, S-wave velocity and density as estimated from the available well-log data. We successfully tested this new algorithm on a real pre-stack seismic dataset acquired over a deep-water turbidite oil reservoir. The results show a good convergence between real and synthetic seismic and the retrieved high resolution petro-elastic models agree with previous studies performed with commercial inversion algorithms.

Mestrado em Design Esta dissertação consiste no desenho de um sistema produto/serviço colaborativo – criado para a Ilha Terceira –, e dos elementos gráficos necessários, na área do turismo acessível. O “Tourceira” destina-se a todos os turistas, com ou sem limitações, que visitam a ilha, e que pretendem realizar actividades turísticas durante a sua estadia. Este serviço destina-se também a todos os habitantes que pretendem fazer parte do projecto, e ser um anfitrião acolhendo os visitantes. Vem combater a escassa oferta de actividades acessíveis e o distanciamento entre o turista, que viaja em excursões, e o residente. Este serviço ambiciona proporcionar novas experiências aos turistas, explorando a Ilha e os cinco sentidos do visitante, e estabelecer relações de maior proximidade entre o turista, a comunidade e a ilha. This dissertation consists of the design of a collaborative product-service system – created to Terceira Island – and the graphic elements needed, in Accessible Tourism field. The “Tourceira” is aimed for all the tourists, with or without limitations, visiting the island, and those who want to do touristic activities during their stay. This Service is also intended for all the resident people, who want to be a part of the project, and be a host welcoming visitors. The “Tourceira” intends to fight the scarcity of accessible activities and the gap between the tourist, who travels on tours, and the resident. This service aims to provide new experiences to tourists, exploring the island and the visitor’s five senses, and establish closer relations between the tourists, the community and the island.

Due to the nature of seismic inversion problems, there are multiple possible solutions that can equally fit the observed seismic data while diverging from the real subsurface model. Consequently, it is important to assess how inverse-impedance models are converging toward the real subsurface model. For this purpose, we evaluated a new methodology to combine the multidimensional scaling (MDS) technique with an iterative geostatistical elastic seismic inversion algorithm. The geostatistical inversion algorithm inverted partial angle stacks directly for acoustic and elastic impedance (AI and EI) models. It was based on a genetic algorithm in which the model perturbation at each iteration was performed recurring to stochastic sequential simulation. To assess the reliability and convergence of the inverted models at each step, the simulated models can be projected in a metric space computed by MDS. This projection allowed distinguishing similar from variable models and assessing the convergence of inverted models toward the real impedance ones. The geostatistical inversion results of a synthetic data set, in which the real AI and EI models are known, were plotted in this metric space along with the known impedance models. We applied the same principle to a real data set using a cross-validation technique. These examples revealed that the MDS is a valuable tool to evaluate the convergence of the inverse methodology and the impedance model variability among each iteration of the inversion process. Particularly for the geostatistical inversion algorithm we evaluated, it retrieves reliable impedance models while still producing a set of simulated models with considerable variability.

Seismic inversion algorithms have been playing a key role in the characterization of oil and gas reservoirs, where a high accuracy is often required to support the decision about the optimal well locations. Since these algorithms usually rely on computer simulations that generate, process and store significant amounts of data, their usage is often limited by their long execution times. In fact, the acceleration of these algorithms allows not only a faster execution, but also the development of larger and more accurate models of the subsurface. This paper proposes a novel parallelization approach of a state of art Stochastic Seismic Amplitude versus Offset Inversion algorithm, by using heterogeneous computing platforms based on a unified OpenCL programming framework. To take full advantage of the computational power made available by systems composed by multiple and possibly different accelerators, a spatial division of the simulation space is performed, enabling the parallel simulation of multiple regions of the geological model. This allows achieving a performance speed-up of 22.8× using two distinct GPUs without compromising the accuracy of the obtained models.

In seismic reservoir characterization one aims to derive petrophysical properties of hydrocarbon reservoir conditioned to both available well-log and seismic reflection data. In this paper we present a new global pre-stack seismic AVO inversion methodology that inverts pre-stack seismic data directly for density, p-wave velocity, s-wave velocity and facies models. The retrieved subsurface models are able to reproduce the high vertical resolution of the well-log data with the spatial coverage of the seismic data. This new approach is based on an iterative genetic algorithm where the perturbation is done recurring to geostatistical simulation and co-simulation. The inverted facies model is an intrinsic part of the solution and represents the link between the elastic models (observed seismic) and the subsurface geology.

Numerical reservoir models are nowadays a common tool in any oil and gas reservoir characterization project. Geostatistical seismic inversion is one of the available methodologies that allow one to retrieve acoustic and/or elastic three-dimensional models from seismic reflection data. Then, the standard approach is the transformation of these impedance models into petrophysical ones (e.g. porosity, facies) following a pre-defined transformation technique. The use of geostatistical, or stochastic, inverse methodologies have the advantage of assessing the uncertainty associated with those inverted elastic models. Stochastic inverse models integrate the very high vertical resolution provided by the well-log data while at the same time are spatially constrained by the seismic data allowing a better reservoir characterization in terms of its spatial distribution. In this work, we present a new iterative geostatistical seismic AVO inversion methodology, based on direct sequential simulation and co-simulations, which allows inverting pre-stack seismic data, angle gathers, for density, p-wave, s-wave and facies models directly. The developed workflow was successfully applied to a synthetic seismic dataset, with known density, Vp, Vs and facies models, to where a considerable amount of random white noise was added. This algorithm is now being tested in a real pre-stack seismic dataset showing promising results.

One of the main challenge problems in geophysics is getting reliable seismic inverse models while the uncertainty is assessed. Seismic inverse problems may be tackled in a probabilistic framework resulting in a set of equiprobable acoustic and elastic impedance models. Here we show a new geostatistical seismic AVO method from where density, Vp and Vs models are retrieved. With the resulting Earth models we also compute the correspondent synthetic pre-stack data and the zero-reflectivity R(0) and Gradient (G) models. We successfully applied this workflow to a 3D synthetic seismic dataset from where density, Vp and Vs models were known. The final best models achieved a global correlation between the original and the synthetic seismograms of about 0.80.

Simulation of continuous variables conditioned to meander structures is an important tool in the context of soil contamination assessment, namely, when the contamination is related with depositional sediments in water channels. Hence, this paper proposes using bi-point statistics stochastic simulation with local anisotropy trends to simulate continuous variables inside predefined channels. To accomplish this objective, the Direct Sequential Simulation (DSS) algorithm was modified to account for local anisotropy when searching for the simulation node. This methodological approach was applied to the spatial characterization of polluted sediments in a coastal lagoon located in the North of Portugal (Barrinha de Esmoriz).

The recently proposed Geostatistical seismic AVO inversion algorithm uses direct sequential simulation and co-simulation as the model parameter space perturbation technique. To reduce the execution time of this iterative geostatistical inversion procedure, a simplified version of the sequential simulation algorithm was parallelized to exploit multi-core Central Processing Units (CPUs). By applying a straightforward functional decomposition of the algorithm, an acceleration of up to 3.5x was observed for a quad-core CPU. This solution is limited not only in scalability but also in the capacity to exploit modern heterogeneous computing systems composed of multiple processors. An efficient parallelization approach of the geostatistical seismic AVO inversion algorithm is here proposed, by considering highly heterogeneous platforms composed by several devices with different computational capabilities. Such a flexible solution is achieved by using the OpenCL API, allowing each part of the algorithm to be easily migrated among the several coexisting CPUs and GPUs.

In order to optimize the development plans of complex, and unconventional, oil and gas reservoirs it is essential to achieve reliable 3D geomechanical models. These models need to comprise the description of the subsurface geology (e.g. lithologies and petro-elastic properties) and simultaneously the in-situ stress fields affecting the reservoir. Currently, the available geomechanical models are often too simplistic and built based exclusively on sparse well-log data. Therefore, they do not translate properly the complexities and heterogeneities of the subsurface geology for the entire reservoir area. We present herein a novel approach to model seismic-driven 3D geomechanical models based on geostatistical seismic AVO inversion. This novel iterative geostatistical inversion methodology allows retrieving density, P-wave and S-wave velocity models from where dynamic moduli properties can be calculated. The resulting models are then used to derive horizontal and vertical stress field models. It is important to highlight that the resulting seismic-driven 3D geomechanical models are simultaneously conditioned locally at the well locations by previously modeled 1D geomechanical models and by the available seismic reflection data for the rest of the reservoir area. The proposed workflow has been successfully tested on synthetic and real pre-stack seismic data. The synthetic example shows that the resulting high resolution inverse elastic models, as well as the dynamic moduli properties, match the real ones. The inverted petro-elastic models derived from the real pre-stack seismic data allowed a better characterization and management of the reservoir in study.

Abnormal pore pressures can result in drilling problems such as borehole instability, stuck pipe, circulation loss, kicks, and blowouts. Gradient pore pressure prediction is of great importance for risk evaluation and for planning new wells in early stages of development and production of oil reservoirs. In this paper, a stochastic simulation with point distributions method is presented to integrate uncertain data in pore pressure cube characterization. The method consists in the use of direct sequential simulation with point distributions. Wells data, in this case, are considered “soft” data, of which uncertainty is quantified by local probability distribution functions or a set of values. A case study using a real dataset is also presented to illustrate the results.

Geostatistical seismic inversion techniques are conventional tools for seismic reservoir characterization and able to integrate well and seismic reflection data while inferring highly variable petro-elastic models from both post- and pre-stack seismic reflection data. In this framework the model parameter space is perturbed by stochastic sequential simulation and co-simulation. While it allows assessing the spatial uncertainty for each property, the use of stochastic sequential simulation and co-simulation as the perturbation engine of the model parameter space assumes: a given spatial continuity pattern for each property of interest, as revealed by a variogram model; that the distribution of each property is known as interpreted from the existing well log-data; and that there are no uncertainties during seismic interpretation used for the regionalization of inversion area based on main seismic reflections and statistical properties. Here we show a brief overview of geostatistical seismic inversion methodologies able to invert seismic reflection data directly for subsurface elastic and rock properties discussing its advantages and disadvantages. Then, we point the road ahead and how these approached can be coupled with adaptive stochastic sampling to quantify uncertainty in geological parameters which are frequently assumed to be true.

Seismic inversion methodologies are key tools in reservoir modeling and characterization for inferring the Earth’s subsurface petro-elastic properties of interest. While deterministic seismic inversion methodologies allow retrieving a single best-fit inverse model with low computational effort, geostatistical inversion procedures are computationally expensive but allow assessing the uncertainty related with the inverse solution. In this work we propose a new approach to accelerate and increase the convergence rate of traditional iterative geostatistical seismic inversion methodologies by integrating an initial guess model (e.g. low-frequency model, a best-fit deterministic solution) as part of the objective function used during the iterative procedure. We show the application of the proposed methodology in both synthetic and real case applications, illustrating its advantages in order to decrease the inversion time while simultaneously increasing the convergence of the iterative geostatistical seismic inversion methodology, as revealed by the global correlation coefficient between real and synthetic seismic data. The application of using geostatistical seismic inversion methodologies by integrating an initial guess model can be considered as the link between deterministic and geostatistical seismic inversion methodologies, taking advantage both inversion solutions in order to retrieve a more realistic approximation of the subsurface geology, decreasing uncertainty and consequently the drilling risks.

Summary Seismic inversion at early exploration stages is still a research challenge, given the lack, or total absence of conditioning data for the inversion. In this project are performed two different approaches of seismic inversion: a deterministic, given by a model-based inversion and a stochastic executed following a global stochastic inversion methodology. Global Stochastic Inversion was executed considering three cases:1) using the second pseudo-log and as low frequency model the initial model used in the model-based inversion, 2)using a model of geological zones and conditioning the GSI to the local acoustic impedance distributions taken from the second pseudo-log, 3)using as input the output from the deterministic inversion and performing the simulations and co-simulations with direct sequential simulation with locally variable mean. The results show that the technique allows to explore different scenarios regarding the spatial distribution of acoustic impedances and assessing the corresponding uncertainty of them. Another important issue observed in the results is about the quality of seismic data: in zones where this doubtful the variability of final acoustic impedances images is high, being poorly matched at the end of the inversion process and do not reproducing the noise in the final result.